Information processing method, medical image diagnostic apparatus, and information processing system

ABSTRACT

An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.

FIELD

Embodiments described herein relate generally to an informationprocessing method, a medical image diagnostic apparatus, and aninformation processing system.

BACKGROUND

A medical image acquired from a subject by a medical image diagnosticapparatus may include noises due to various factors. In recent years, anoise reduction processing model based on machine learning has beenproposed as one of noise reduction methods for reducing such noises.However, in order to obtain the noise reduction processing model, it isnecessary to prepare training data used for the machine learning.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary configuration of an X-ray CTapparatus according to an exemplary embodiment described below.

FIG. 2 illustrates an exemplary process performed by the X-ray CTapparatus.

FIG. 3 illustrates an exemplary process of generating noise dataaccording to an exemplary embodiment described below.

FIG. 4A illustrates a training process according to an exemplaryembodiment described below.

FIG. 4B illustrates a training process according to an exemplaryembodiment described below.

FIG. 4C illustrates a training process according to an exemplaryembodiment described below.

FIG. 5A illustrates a training process according to an exemplaryembodiment described below.

FIG. 5B illustrates a training process according to an exemplaryembodiment described below.

FIG. 5C illustrates a training process according to an exemplaryembodiment described below.

FIG. 5D illustrates a training process according to an exemplaryembodiment described below.

FIG. 6A illustrates a noise reduction process according to an exemplaryembodiment described below.

FIG. 6B illustrates a noise reduction process according to an exemplaryembodiment described below.

FIG. 7 illustrates a process of an X-ray CT apparatus according to anexemplary embodiment described below.

FIG. 8 is a block diagram of an exemplary configuration of an X-ray CTapparatus according to another exemplary embodiment described below.

DETAILED DESCRIPTION

An information processing method of an embodiment is a processing methodof information acquired by imaging performed by a medical imagediagnostic apparatus, the information processing method includes thesteps of: on the basis of first subject data acquired by the imagingperformed by the medical image diagnostic apparatus, acquiring noisedata in the first subject data; on the basis of second subject dataacquired by the imaging performed by a medical image diagnostic modalitysame kind as the medical image diagnostic apparatus and the noise data,acquiring synthesized subject data in which noises based on the noisedata are added to the second subject data; and acquiring a noisereduction processing model by machine learning using the synthesizedsubject data and third subject data acquired by the imaging performed bythe medical image diagnostic modality.

Hereinafter, with reference to the accompanying drawings, an embodimentof an information processing method, a medical image diagnosticapparatus, and an information processing system will be described indetail.

In the present embodiment, X-ray CT will be described as an example of amedical image diagnostic modality. That is, in the present embodiment,an information processing method of information acquired by imagingperformed by the X-ray CT will be described.

The X-ray CT is implemented, for example, in an X-ray CT apparatus 10illustrated in FIG. 1 . FIG. 1 is a block diagram illustrating anexample of a configuration of the X-ray CT apparatus 10 according to afirst embodiment. For example, the X-ray CT apparatus 10 has a gantry110, a bed 130, and a console 140.

In FIG. 1 , it is assumed that the longitudinal direction of a rotatingshaft of a rotating frame 113 or a tabletop 133 of the bed 130 in anon-tilted state is a Z axis direction. Furthermore, it is assumed thatan axial direction orthogonal to the Z axis direction and horizontal toa floor surface is an X axis direction. Furthermore, it is assumed thatan axial direction orthogonal to the Z axis direction and perpendicularto the floor surface is a Y axis direction. Note that FIG. 1 illustratesthe gantry 110 drawn from a plurality of directions for convenience ofdescription and the X-ray CT apparatus 10 has one gantry 110.

The gantry 110 includes an X-ray tube 111, an X-ray detector 112, therotating frame 113, an X-ray high voltage device 114, a control device115, a wedge 116, a collimator 117, and a data acquisition system (DAS)118.

The X-ray tube 111 is a vacuum tube having a cathode (filament) thatgenerates thermoelectrons and an anode (target) that generates X-rays inresponse to a collision of thermoelectrons. The X-ray tube 111 emits thethermoelectrons toward the anode from the cathode by the application ofa high voltage from the X-ray high voltage device 114, therebygenerating the X-rays to be emitted to a subject P.

The X-ray detector 112 detects the X-rays emitted from the X-ray tube111 and passed through the subject P, and outputs a signal correspondingto the dose of the detected X-rays to the DAS 118. The X-ray detector112, for example, includes a plurality of detection element arrays inwhich a plurality of detection elements are arranged in a channeldirection (channel direction) along one arc centered on a focal point ofthe X-ray tube 111. The X-ray detector 112, for example, has a structurein which the detection element arrays with the detection elementsarranged in the channel direction are arranged in a row direction (slicedirection and row direction).

For example, the X-ray detector 112 is an indirect conversion typedetector having a grid, a scintillator array, and a photosensor array.The scintillator array has a plurality of scintillators. Each of thescintillators has a scintillator crystal that outputs light with aphoton quantity corresponding to an incident X-ray dose. The grid has anX-ray shielding plate that is disposed on the surface of thescintillator array on an X-ray incident side and absorbs scatted X-rays.The grid may also be referred to as a collimator (a one-dimensionalcollimator or a two-dimensional collimator). The photosensor array has afunction of converting light into an electrical signal corresponding tothe amount of light from the scintillator, and has, for example,photosensors such as photodiodes. Note that the X-ray detector 112 maybe a direct conversion type detector having a semiconductor element thatconverts the incident X-rays into electrical signals.

The rotating frame 113 is an annular frame that supports the X-ray tube111 and the X-ray detector 112 so as to face each other and rotates theX-ray tube 111 and the X-ray detector 112 by the control device 115. Forexample, the rotating frame 113 is a casting made of aluminum. Note thatthe rotating frame 113 can further support the X-ray high voltage device114, the wedge 116, the collimator 117, the DAS 118 and the like, inaddition to the X-ray tube 111 and the X-ray detector 112. Moreover, therotating frame 113 can further support various configurations notillustrated in FIG. 1 . Hereinafter, in the gantry 110, the rotatingframe 113 and a part, which rotationally moves with the rotating frame113, are also referred to as a rotating part.

The X-ray high voltage device 114 has electric circuitry such as atransformer and a rectifier, and has a high voltage generation devicethat generates a high voltage to be applied to the X-ray tube 111 and anX-ray control device that controls an output voltage corresponding tothe X-rays generated by the X-ray tube 111. The high voltage generationdevice may be a transformer type device or an inverter type device. Notethat the X-ray high voltage device 114 may be provided on the rotatingframe 113, or may also be provided on a fixed frame (not illustrated).

The control device 115 has processing circuitry having a centralprocessing unit (CPU) and the like, and a driving mechanism such as amotor and an actuator. The control device 115 receives input signalsfrom an input interface 143 and controls the operations of the gantry110 and the bed 130. For example, the control device 115 controls therotation of the rotating frame 113, the tilt of the gantry 110, theoperation of the bed 130, and the like. As an example, as control fortilting the gantry 110, the control device 115 rotates the rotatingframe 113 around an axis parallel to the X axis direction based oninformation on an input inclination angle (tilt angle). Note that thecontrol device 115 may be provided in the gantry 110 or may also beprovided in the console 140.

The wedge 116 is an X-ray filter for adjusting the dose of the X-raysemitted from the X-ray tube 111. Specifically, the wedge 116 is an X-rayfilter that attenuates the X-rays emitted from the X-ray tube 111 suchthat the X-rays emitted from the X-ray tube 111 to the subject P have apredetermined distribution. For example, the wedge 116 is a wedge filteror a bow-tie filter and is manufactured by processing aluminum and thelike to have a predetermined target angle and a predetermined thickness.

The collimator 117 is a lead plate and the like for narrowing down theemission range of the X-rays having transmitted through the wedge 116and forms a slit by a combination of a plurality of lead plates and thelike. Note that the collimator 117 may also be referred to as an X-raydiaphragm. Furthermore, although FIG. 1 illustrates a case where thewedge 116 is disposed between the X-ray tube 111 and the collimator 117,the collimator 117 may be disposed between the X-ray tube 111 and thewedge 116. In such a case, the wedge 116 attenuates the X-rays, whichare emitted from the X-ray tube 111 and whose emission range is limitedby the collimator 117, by allowing the X-rays to pass therethrough.

The DAS 118 acquires X-ray signals detected by each detector elementincluded in the X-ray detector 112. For example, the DAS 118 has anamplifier that performs an amplification process on electrical signalsoutput from each detector element and an A/D converter that converts theelectrical signals to digital signals, and generates detection data. TheDAS 118 is implemented by, for example, a processor.

The data generated by the DAS 118 is transmitted from a transmitterhaving a light emitting diode (LED) provided on the rotating frame 113to a receiver having a photodiode provided on a non-rotating part (forexample, a fixed frame and the like and not illustrated in FIG. 1 ) ofthe gantry 110 by optical communication, and is transmitted to theconsole 140. The non-rotating part is, for example, a fixed frame andthe like that rotatably supports the rotating frame 113. Note that thedata transmission method from the rotating frame 113 to the non-rotatingpart of the gantry 110 is not limited to the optical communication, andmay adopt any non-contact type data transmission method or a contacttype data transmission method.

The bed 130 is a device that places and moves the subject P to bescanned and includes a pedestal 131, a couch driving device 132, thetabletop 133, and a support frame 134. The pedestal 131 is a casing thatsupports the support frame 134 so as to be movable in a verticaldirection. The couch driving device 132 is a driving mechanism thatmoves the tabletop 133, on which the subject P is placed, in a long axisdirection of the tabletop 133 and includes a motor, an actuator and thelike. The tabletop 133 provided on the upper surface of the supportframe 134 is a plate on which the subject P is placed. Note that thecouch driving device 132 may also move the support frame 134 in the longaxis direction of the tabletop 133 in addition to the tabletop 133.

The console 140 has a memory 141, a display 142, the input interface143, and processing circuitry 144. Although the console 140 is describedas a separate body from the gantry 110, the gantry 110 may include theconsole 140 or a part of each component of the console 140.

The memory 141 is implemented by, for example, a semiconductor memoryelement such as a random access memory (RAM) and a flash memory, a harddisk, an optical disk, and the like. For example, the memory 141 storesa computer program for circuitry included in the X-ray CT apparatus 10to perform its functions. Furthermore, the memory 141 stores variousinformation obtained by imaging the subject P. Furthermore, the memory141 stores a noise reduction processing model generated by theprocessing circuitry 144 to be described below. Note that the memory 141may be implemented by a server group (cloud) connected to the X-ray CTapparatus 10 via a network.

The display 142 displays various information. For example, the display142 displays an image based on denoised data to be described below.Furthermore, for example, the display 142 displays a graphical userinterface (GUI) for receiving various instructions, settings, and thelike from a user via the input interface 143. For example, the display142 is a liquid crystal display or a cathode ray tube (CRT) display. Thedisplay 142 may be a desktop type display, or may be composed of atablet terminal and the like capable of wirelessly communicating withthe body of the X-ray CT apparatus 10.

Although the X-ray CT apparatus 10 is described as including the display142 in FIG. 1 , the X-ray CT apparatus 10 may include a projectorinstead of or in addition to the display 142. Under the control of theprocessing circuitry 144, the projector can perform projection onto ascreen, a wall, a floor, the body surface of the subject P, and thelike. As an example, the projector can also perform projection onto anyplane, object, space, and the like by projection mapping.

The input interface 143 receives various input operations from a user,converts the received input operations into electrical signals, andoutputs the electrical signals to the processing circuitry 144. Forexample, the input interface 143 is implemented by a mouse, a keyboard,a trackball, a switch, a button, a joystick, a touch pad for performingan input operation by touching an operation surface, a touch screen inwhich a display screen and a touch pad are integrated, non-contact inputcircuitry using an optical sensor, voice input circuitry, and the like.Note that the input interface 143 may be composed of a tablet terminaland the like capable of wirelessly communicating with the body of theX-ray CT apparatus 10. Furthermore, the input interface 143 may becircuitry that receives an input operation from a user by motioncapture. As an example, the input interface 143 can receive a user'sbody movement, line of sight, and the like as an input operation byprocessing a signal acquired via a tracker or an image collected for auser. Furthermore, the input interface 143 is not limited to oneincluding physical operation parts such as a mouse and a keyboard. Forexample, an example of the input interface 143 includes electric signalprocessing circuitry which receives an electric signal corresponding toan input operation from an external input device separately providedfrom the X-ray CT apparatus 10 and outputs the electric signal to theprocessing circuitry 144.

The processing circuitry 144 controls the overall operation of the X-rayCT apparatus 10 by performing a control function 144 a, an imagingfunction 144 b, an acquisition function 144 c, a model generationfunction 144 d, a noise reduction processing function 144 e, and anoutput function 144 f.

For example, the processing circuitry 144 reads a computer programcorresponding to the control function 144 a from the memory 141 andexecutes the read computer program, thereby controlling variousfunctions, such as the imaging function 144 b, the acquisition function144 c, the model generation function 144 d, the noise reductionprocessing function 144 e, and the output function 144 f, on the basisof various input operations received from a user via the input interface143.

Furthermore, for example, the processing circuitry 144 reads a computerprogram corresponding to the imaging function 144 b from the memory 141and executes the read computer program, thereby imaging the subject P.For example, the imaging function 144 b controls the X-ray high voltagedevice 114 to supply the X-ray tube 111 with a high voltage. With this,the X-ray tube 111 generates X-rays to be emitted to the subject P.Furthermore, the imaging function 144 b controls the couch drivingdevice 132 to move the subject P into an imaging port of the gantry 110.Furthermore, the imaging function 144 b adjusts the position of thewedge 116 and the opening degree and position of the collimator 117,thereby controlling the distribution of the X-rays emitted to thesubject P. Furthermore, the imaging function 144 b controls the controldevice 115 to rotate the rotating part. Furthermore, while the imagingis performed by the imaging function 144 b, the DAS 118 acquires X-raysignals from the respective detection elements in the X-ray detector 112and generates detection data.

Furthermore, the imaging function 144 b performs pre-processing on thedetection data output from the DAS 118. For example, the imagingfunction 144 b performs pre-processing, such as logarithmictransformation processing, offset correction processing, inter-channelsensitivity correction processing, and beam hardening correction, on thedetection data output from the DAS 118. Note that the data subjected tothe pre-processing is also described as raw data. Furthermore, thedetection data before the pre-processing and the raw data subjected tothe pre-processing are also collectively described as projection data.

Furthermore, for example, the processing circuitry 144 reads a computerprogram corresponding to the acquisition function 144 c from the memory141 and executes the read computer program, thereby acquiring noise dataon the basis of first subject data obtained by imaging the subject P andacquiring synthesized subject data on the basis of second subject dataobtained by imaging the subject P and the noise data. Furthermore, forexample, the processing circuitry 144 reads a computer programcorresponding to the model generation function 144 d from the memory 141and executes the read computer program, thereby obtaining the noisereduction processing model by machine learning using the synthesizedsubject data and third subject data obtained by imaging the subject P.Furthermore, for example, the processing circuitry 144 reads a computerprogram corresponding to the noise reduction processing function 144 efrom the memory 141 and executes the read computer program, therebyreducing noises in input subject data by the noise reduction processingmodel and obtaining denoised data. Furthermore, for example, theprocessing circuitry 144 reads a computer program corresponding to theoutput function 144 f from the memory 141 and executes the read computerprogram, thereby outputting an image based on the denoised data. Detailsof processing performed by the acquisition function 144 c, the modelgeneration function 144 d, the noise reduction processing function 144e, and the output function 144 f will be described below.

In the X-ray CT apparatus 10 illustrated in FIG. 1 , the respectiveprocessing functions are stored in the memory 141 in the form of thecomputer programs executable by a computer. The processing circuitry 144is a processor that performs a function corresponding to each computerprogram by reading and executing the computer program from the memory141. In other words, the processing circuitry 144 having read thecomputer program has a function corresponding to the read computerprogram.

Note that, in FIG. 1 , it has been described that the control function144 a, the imaging function 144 b, the acquisition function 144 c, themodel generation function 144 d, the noise reduction processing function144 e, and the output function 144 f are implemented by the singleprocessing circuitry 144, but the processing circuitry 144 may beconfigured by combining a plurality of independent processors, and eachprocessor may be configured to perform each function by executing eachcomputer program. Furthermore, each processing function of theprocessing circuitry 144 may be performed by being appropriatelydistributed or integrated into a single circuit or a plurality ofprocessing circuits.

Furthermore, the processing circuitry 144 may also perform the functionsby using a processor of an external device connected via the network.For example, the processing circuitry 144 reads and executes thecomputer program corresponding to each function from the memory 141 anduses, as computation resources, a server group (cloud) connected to theX-ray CT apparatus 10 via the network, thereby performing each functionillustrated in FIG. 1 .

Furthermore, although FIG. 1 illustrates only the single memory 141, theX-ray CT apparatus 10 may include a plurality of physically separatedmemories. For example, the X-ray CT apparatus 10 may separately include,as the memory 141, a memory that stores a computer program required whencircuitry included in the X-ray CT apparatus 10 performs its function, amemory that stores various information obtained by imaging the subjectP, and a memory that stores the noise reduction processing model.

So far, the configuration example of the X-ray CT apparatus 10 has beendescribed. Under such a configuration, it is assumed that the processingcircuitry 144 in the X-ray CT apparatus 10 can easily acquire ahigh-quality noise reduction processing model by the following processesto be described below.

First, a series of processes from the imaging of the subject P to theoutput of an image will be described with reference to FIG. 2 . FIG. 2is a diagram illustrating an example of a process by the X-ray CTapparatus 10. As illustrated in FIG. 2 , the process by the X-ray CTapparatus 10 is roughly divided into a reconstruction process and atraining process.

For example, in the reconstruction process, the imaging function 144 bobtains projection data by imaging the subject P. Next, the noisereduction processing function 144 e generates a reconstructed image (CTimage data) by performing the reconstruction processing on theprojection data. For example, the noise reduction processing function144 e generates the reconfigured image by performing the reconstructionprocessing using a filtered back-projection (FBP) method, a successiveapproximation reconstruction method, a successive approximation appliedreconstruction method, and the like on the projection data. Furthermore,the noise reduction processing function 144 e can also generate thereconfigured image by performing the reconstruction processing by amachine learning method. For example, the noise reduction processingfunction 144 e generates the reconstructed image by a deep learningreconstruction (DLR) method.

The reconstructed image may include noises due to various factors. Forexample, although the image quality of the reconstructed image isimproved as the dose of X-rays used for acquiring the projection dataincreases, it is preferable to suppress the dose of the X-rays from thestandpoint of reducing the exposure dose of the subject P. Then, whenthe projection data is acquired using a low dose of X-rays, thereconstructed image may include noises. Furthermore, a high-accuracyreconstruction method such as the successive approximationreconstruction method generally has a high computational load, and forexample, when it is desired to quickly acquire the reconstructed image,another low-accuracy reconstruction method is selected. Then, when thelow-accuracy reconstruction method is used, the reconstructed image mayinclude noises.

In this regard, the noise reduction processing function 144 e performsnoise reduction processing on the reconstructed image as illustrated inFIG. 2 . For example, the noise reduction processing function 144 eperforms the noise reduction processing on the reconstructed image bythe noise reduction processing model trained using training data. Withthis, the output function 144 f can output an image on the basis of areconstructed image with reduced noises. For example, the outputfunction 144 f generates a display image on the basis of thereconstructed image with reduced noise and allows the display 142 todisplay the display image.

In the following description, as an example, the noise reductionprocessing model is configured by a deep convolution neural network(DCNN) illustrated in FIG. 2 . For example, the model generationfunction 144 d performs the training process prior to the reconstructionprocess, thereby generating a DCNN that is functionalized to reducenoises in input data. Furthermore, the generated DCNN is stored in thememory 141, for example, and the noise reduction processing function 144e can appropriately read and use the DCNN.

The training data of FIG. 2 is composed of, for example, a pair of cleandata not substantially including noises and noisy data including noises.For example, the clean data is a reconstructed image acquired using ahigh dose of X-rays and the noisy data is a reconstructed image acquiredusing a low dose of X-rays. Alternatively, the noisy data may be asimulation image generated by a noise simulator. For example, the noisesimulator receives the input of the clean data and simulates noises,thereby generating noise-added noisy data. In such a case, the noisereduction processing function 144 e can train the DCNN by deep learningan input of which is the noisy data and a target of which is the cleandata. Note that a training method targeting the clean data is alsodescribed as noise-to-clean (N2C).

As another example, the training data of FIG. 2 is composed of a pair offirst noisy data including noises and second noisy data including othernoises independent of noises in the first noisy data. These two piecesof noisy data can be generated by the noise simulator, for example. Insuch a case, the noise reduction processing function 144 e can train theDCNN by deep learning an input of which is one noisy data and a targetof which is the other noisy data. Note that a training method targetingthe noisy data is also described as noise-to-noise (N2N).

However, it is not easy to acquire a required number of clean data fortraining the DCNN. This is because there are not many opportunities toperform high-dose imaging in clinical sites. Furthermore, there areimaging conditions and imaging parts where there are particularly fewopportunities to perform the high-dose imaging. For example, thehigh-dose imaging is rarely performed on a part easily affected byX-rays such as eyes and bone marrow. Furthermore, even when thehigh-dose imaging is performed, noises may occur.

Furthermore, it is not easy to prepare the noisy data by simulation.That is, unless a complicated model is used, it is not possible toperform appropriate noise simulation, and there are imaging conditions,imaging parts and the like that are difficult to be accurately modeled.Unless the noise simulation is appropriately performed, the accuracy ofthe DCNN may also be reduced.

Particularly, it is difficult to simulate a plurality of independentnoises. For example, when simulating the noises on the basis of theclean data, the clean data may include noises. The noises included inthe clean data serve as an obstacle in simulating the independentnoises. Furthermore, in recent years, there are cases where verylow-dose imaging is performed, whereas it is particularly difficult tosimulate noises that occur in the very low-dose imaging.

That is, even when either the noise-to-clean training method and thenoise-to-noise training method is adopted, it is not easy to preparetraining data and it is difficult to train the DCNN appropriately. Inthis regard, the processing circuitry 144 makes it possible to acquiretraining data by a process to be described below and easily acquire ahigh quality DCNN. Specifically, the processing circuitry 144 acquiresnoise data on the basis of the first subject data, acquires synthesizedsubject data on the basis of the second subject data and the noise data,and acquires a DCNN by performing deep learning using the synthesizedsubject data and the third subject data.

First, a noise data acquisition process based on the first subject datawill be described with reference to FIG. 3 . FIG. 3 is a diagram forexplaining noise data according to the first embodiment. In FIG. 3 ,projection data Y1 will be described as an example of the first subjectdata. The projection data Y1 is obtained by imaging that is performed bythe X-ray CT apparatus 10, for example.

Here, the dose of X-rays used for acquiring the projection data Y1, anoise level of the projection data Y1, and the like are not particularlylimited. For example, the imaging function 144 b acquires the projectiondata Y1 by imaging a subject P11 by using a low dose of X-rays. Notethat the subject P11 is an example of a subject P1. For example, asillustrated in FIG. 3 , the projection data Y1 can be illustrated as asinogram in which the channel direction of the X-ray detector 112 is setas a horizontal axis and the view (X-ray irradiation angle) is set as avertical axis.

For example, the acquisition function 144 c acquires projection data Y11and projection data Y12 by sampling the projection data Y1. As anexample, the acquisition function 144 c acquires the projection data Y11by sampling odd view data in the projection data Y1 and acquires theprojection data Y12 by sampling even view data in the projection dataY1. That is, the acquisition function 144 c alternately samples theprojection data Y11 and the projection data Y12 for each view in theprojection data Y1. Note that the projection data Y11 and the projectiondata Y12 are examples of a first subset and a second subset. Theprojection data Y11 and the projection data Y12 are data having a viewnumber corresponding to a half of the projection data Y1.

Note that the sampling of the projection data Y1 can be variouslymodified. For example, the acquisition function 144 c may alternatelysample the projection data Y11 and the projection data Y12 for each of aplurality of views in the projection data Y1. Furthermore, for example,the acquisition function 144 c may alternately sample the projectiondata Y11 and the projection data Y12 for each random number of views inthe projection data Y1.

Furthermore, the acquisition function 144 c may sample all the views ofthe projection data Y1, or sample some of the views of the projectiondata Y1. For example, when the projection data Y1 is full data of“360°”, the acquisition function 144 c may perform sampling within arange in which half reconstruction can be performed. As an example, whena fan angle is “30°”, the acquisition function 144 c can extract a“210°” view starting from an arbitrary view in the projection data Y1 of“360°”, and sample the projection data Y11 and the projection data Y12from the “210°” view. Here, the acquisition function 144 c can shift thestarting point for extracting the “210°” view, thereby extracting aplurality of “210°” views. That is, the acquisition function 144 c canacquire a plurality of pairs of the projection data Y11 and theprojection data Y12 from the projection data Y1.

Next, the acquisition function 144 c performs reconstruction processingon each of the projection data Y11 and the projection data Y12, therebyacquiring a reconstructed image X11 and a reconstructed image X12. Forexample, the acquisition function 144 c performs the reconstructionprocessing by the FBP method, thereby acquiring the reconstructed imageX11 and the reconstructed image X12. Note that the reconstructed imageX11 and the reconstructed image X12 are examples of a firstreconstructed image and a second reconstructed image.

Next, the acquisition function 144 c acquires noise data ε on the basisof the reconstructed image X11 and the reconstructed image X12. Thenoise data E is, for example, data indicating noise intensity at eachposition in an image space. That is, the noise data E is not a simplenumerical value, such as an SD value, and is data indicating a spatialdistribution of noises.

For example, the acquisition function 144 c acquires the noise data ε byperforming difference processing between the reconstructed image X11 andthe reconstructed image X12. For example, the acquisition function 144 cacquires the noise data ε by calculating, for each pixel, a differencein pixel values between corresponding pixels between the reconstructedimage X11 and the reconstructed image X12.

Here, the projection data Y11 and the projection data Y12 are dataobtained from the same object, and are data sampled such that overlapdoes not occur. Accordingly, the reconstructed image X11 and thereconstructed image X12 based on the projection data Y11 and theprojection data Y12 have noises independent of each other. For example,the reconstructed image X11 and the reconstructed image X12 have thesame noise level as when imaging is performed with a dose correspondingto a half of the dose used for acquiring the projection data Y1. Notethat there is no need to strictly control the sampling so as not tocause overlap, and small amount of overlap, such as overlap of only oneview, may be allowed.

Note that the noise data ε can also include various image artifacts asnoises. That is, when the image artifacts are included in thereconstructed image X11 and the reconstructed image X12, the noise dataE includes the image artifacts as noises. When such noise data E is usedfor training, DCNN to be described below is functionalized to reducevarious noises including the image artifacts.

As an example, the acquisition function 144 c can acquire the noise dataε by a computation formula of εi=α (x1−x2). εi denotes a pixel value ofthe noise data ε at a position i. Furthermore, x1 denotes a pixel valueof the reconstructed image X11 at the position i. Furthermore, x2denotes a pixel value of the reconstructed image X12 at the position i.

Furthermore, α denotes a parameter for adjusting a noise level. That is,the acquisition function 144 c can generate various noise data ε withadjusted noise levels by adjusting the value of α. For example, when αis set to a value larger than “0.5”, the noise data ε indicates noisesgenerated when imaging is performed with a dose smaller than the doseused for acquiring the projection data Y1. The acquisition function 144c may set α to a fixed value or change the value of α. When changing thevalue of α, the acquisition function 144 c can acquire the noise data εfor each value of α.

As described above, the acquisition function 144 c acquires the noisedata ε on the basis of the projection data Y1 obtained from the subjectP11 by the imaging performed by the X-ray CT apparatus 10. Similarly,the acquisition function 144 c acquires a plurality of noise data on thebasis of a plurality of projection data. For example, as illustrated inFIG. 4A, the acquisition function 144 c performs noise extractionprocessing on each of a plurality of projection data such as projectiondata Yk−1, projection data Yk, and projection data Yk+1, and allows theextracted noise data to be stored in a noise pool 141 a. Note that thenoise pool 141 a is an example of the memory 141. Furthermore, FIG. 4Ais a diagram for explaining a training process according to the firstembodiment.

As an example, the acquisition function 144 c generates volume dataindicating a noise distribution for each of the projection data such asthe projection data Yk−1, the projection data Yk, and the projectiondata Yk+1, and allows a plurality of two-dimensional data obtained bydividing the volume data to be stored in an image pool 141 b as noisedata. Alternatively, the acquisition function 144 c generates volumedata indicating a noise distribution for each of the projection datasuch as the projection data Yk−1, the projection data Yk, and theprojection data Yk+1, and allows the volume data to be stored in theimage pool 141 b as noise data. That is, the noise data may be managedas three-dimensional data or two-dimensional data.

Here, the projection data Yk−1, the projection data Yk, and theprojection data Yk+1 illustrated in FIG. 4A are examples of the firstsubject data. The projection data Yk−1, the projection data Yk, and theprojection data Yk+1 may be data acquired from the subject P11, or maybe data acquired from a subject other than the subject P11. Furthermore,the projection data Yk−1, the projection data Yk, and the projectiondata Yk+1 may be data obtained by the imaging performed by the X-ray CTapparatus 10, or may be data obtained by imaging performed by an X-rayCT apparatus different from the X-ray CT apparatus 10. The first subjectdata may be data obtained by low-dose imaging, and thus can be acquiredrelatively easily.

Here, the acquisition function 144 c can also increase the number of thenoise data stored in the noise pool 141 a, by various methods. Forexample, the acquisition function 144 c can change the reconstructionmethod performed when generating the noise data, thereby generating aplurality of noise data. Furthermore, for example, the acquisitionfunction 144 c can rotate the noise data by “90°”, thereby obtainingfour pieces of noise data. With this, the acquisition function 144 c canacquire more various noise data. The acquisition function 144 c mayadjust the number of the noise data stored in the noise pool 141 a to bethe same as the number of the second subject data stored in the imagepool 141 b to be described below.

Next, the second subject data and the third subject data will bedescribed with reference to FIG. 4B. FIG. 4B is a diagram for explaininga training process according to the first embodiment. For example, theacquisition function 144 c first acquires a plurality of projection datasuch as projection data Yl−1, projection data Y1, and projection dataYl+1 illustrated in FIG. 4B. Note that the projection data Yl−1, theprojection data Yl, and the projection data Yl+1 are examples of fourthsubject data.

For example, the projection data Yl−1, the projection data Yl, and theprojection data Yl+1 are data different from the first subject data (forexample, the projection data Yk−1, the projection data Yk, theprojection data Yk+1, and the like). For example, the projection dataYl−1, the projection data Yl, and the projection data Yl+1 are dataacquired from a subject different from the first subject data, or dataacquired from the same subject at different dates and times. In otherwords, the second subject data is data acquired from a subject differentfrom the first subject data, or data acquired at a date and timedifferent from the first subject data. Note that the projection dataYl−1, the projection data Yl, and the projection data Yl+1 may be datathat partially or entirely overlap the first subject data.

Furthermore, the projection data Yl−1, the projection data Yl, and theprojection data Yl+1 may be data obtained by the imaging performed bythe X-ray CT apparatus 10, or may be data obtained by imaging performedby an X-ray CT apparatus different from the X-ray CT apparatus 10. Thatis, the second subject data may be acquired by imaging performed by thesame medical image diagnostic apparatus as the medical image diagnosticapparatus that has imaged the first subject data, or the second subjectdata may be acquired by imaging performed by a same kind of butdifferent medical image diagnostic apparatus as the medical imagediagnostic apparatus that has imaged the first subject data. The secondsubject data may be acquired by the same imaging system as that of thefirst subject data, or may be acquired by a different imaging system.For example, when the first subject data is acquired by helical scan,the second subject data may be collected by non-helical scan.

Next, the acquisition function 144 c performs reconstruction processingR1 and reconstruction processing R2 on each of the projection data. Thereconstruction processing R1 and the reconstruction processing R2 may bedifferent reconstruction methods or the same reconstruction method. Forexample, the acquisition function 144 c performs the FBP as thereconstruction processing R1 and performs the successive approximationreconstruction method as the reconstruction processing R2.

For example, the acquisition function 144 c performs the reconstructionprocessing R1 on the projection data Yl−1, and allows a generatedreconstructed image to be stored in the image pool 141 b. As an example,the acquisition function 144 c divides the reconstructed image generatedas volume data into a plurality of two-dimensional reconstructed imagesand allows the two-dimensional reconstructed images to be stored in theimage pool 141 b. Similarly, the acquisition function 144 c performs thereconstruction processing R1 on the projection data Yl, and allows agenerated reconstructed image to be stored in the image pool 141 b.Similarly, the acquisition function 144 c performs the reconstructionprocessing R1 on the projection data Yl+1, and allows a generatedreconstructed image to be stored in the image pool 141 b. Thereconstructed images generated by the reconstruction processing R1 areexamples of the second subject data. Furthermore, the image pool 141 bis an example of the memory 141.

Furthermore, the acquisition function 144 c performs the reconstructionprocessing R2 on the projection data Yl−1, and allows a generatedreconstructed image to be stored in an image pool 141 c. As an example,the acquisition function 144 c divides the reconstructed image (volumedata) into a plurality of two-dimensional reconstructed images andallows the two-dimensional reconstructed images to be stored in theimage pool 141 c. Similarly, the acquisition function 144 c performs thereconstruction processing R2 on the projection data Yl, and allows agenerated reconstructed image to be stored in the image pool 141 c.Similarly, the acquisition function 144 c performs the reconstructionprocessing R2 on the projection data Yl+1, and allows a generatedreconstructed image to be stored in the image pool 141 c. Thereconstructed images generated by the reconstruction processing R2 areexamples of the third subject data. That is, the third subject data isdata acquired by imaging a same subject as that of the second subjectdata. Furthermore, the image pool 141 c is an example of the memory 141.

Note that the reconstruction field of views (rFOVs) of the secondsubject data and the third subject data may be a fixed size or may bechanged in size. For example, the acquisition function 144 c can alsogenerate a plurality of second subject data, whose rFOVs have beenchanged, from one projection data. With this, the acquisition function144 c can acquire more various data as the second subject data and thethird subject data.

As described above, the acquisition function 144 c allows the noise databased on the first subject data to be stored in the noise pool 141 a,allows the second subject data to be stored in the image pool 141 b, andallows the third subject data to be stored in the image pool 141 c.Next, as illustrated in FIG. 4C, the acquisition function 144 c readsthe noise data and the second subject data from the noise pool 141 a andthe image pool 141 b, and acquires synthesized subject data, in whichnoises based on the noise data are added to the second subject data, onthe basis of the second subject data and the noise data. FIG. 4C is adiagram for explaining a training process according to the firstembodiment.

For example, the acquisition function 144 c acquires the synthesizedsubject data by summing pixel values for each pixel in the noise dataand the second subject data. In other words, the acquisition function144 c acquires the synthesized subject data by synthesizing the noisedata and the second subject data. Here, the acquisition function 144 ccan acquire the synthesized subject data for each combination of thenoise data stored in the noise pool 141 a and the second subject datastored in the image pool 141 b. Furthermore, the acquisition function144 c can also acquire a plurality of synthesized subject data byshifting the position of the noise data with respect to the secondsubject data.

Note that the acquisition function 144 c may adjust a weight whensynthesizing the noise data and the second subject data. For example,the acquisition function 144 c adjusts the noise level of the noise databy the aforementioned parameter a and then adds the noise level to thesecond subject data. As an example, the aforementioned noise datacorresponds to a difference between the reconstructed image X11 and thereconstructed image X12 in FIG. 3 , and has undergone normalization(averaging effect). Note that the addition and the subtraction producesimilar averaging effects. The acquisition function 144 c can correctthe influence of the averaging effect by performing weighting in thesynthesizing process. As another example, the acquisition function 144 ccan generate various synthesized subject data with varying doses byperforming various types of weighting.

Then, as illustrated in FIG. 4C, the model generation function 144 dperforms training using the synthesized subject data and the thirdsubject data read from the image pool 141 c, thereby obtaining a DCNNfunctionalized to perform noise reduction processing. Specifically, themodel generation function 144 d obtains the DCNN by performing deeplearning an input of which is the synthesized subject data and a targetof which is the third subject data. Note that the DCNN illustrated inFIG. 4C is an example of the noise reduction processing model.

Hereinafter, details of the training performed by the model generationfunction 144 d will be described. FIG. 5A to FIG. 5D illustrate atraining process according to an exemplary embodiment described below.

More specifically, FIG. 5A illustrates a general artificial neuralnetwork (ANN) having n inputs, a K^(th) hidden layer, and three outputs.Each layer of the ANN is made up of nodes (also called neurons), andeach node performs a weighted sum of the inputs to produce an output andcompares the result of the weighted sum with a threshold. ANNs make up aclass of functions for which members of the class are acquired byvarying thresholds, connection weights, or specifics of an architecturesuch as the number of nodes and/or their connectivity. The nodes in theANN may be referred to as neurons (or neuronal nodes), and the neuronscan have inter-connections between different layers of the ANN system.For example, the ANN has more than three layers of neurons and has asmany output neurons x to N as input neurons, wherein N is the number ofpixels in the reconstructed image. Synapses (that is, connectionsbetween neurons) store values called “weights” (also interchangeablyreferred to as “coefficients” or “weighting coefficients”) thatmanipulate data in calculations. The outputs of the ANN depend on threetypes of parameters: (i) An interconnection pattern between differentlayers of neurons, (ii) A learning process for updating weights of theinterconnections, and (iii) An activation function that converts aneuron's weight input to its output activation.

Mathematically, a neuron's network function m(x) is defined as acomposition n_(i) (x) of other functions, which can further be definedas a composition of other functions. This can be convenientlyrepresented as a network structure, with arrows depicting dependenciesbetween variables, as illustrated in FIG. 5A. For example, the ANN canuse a nonlinear weighted sum, wherein m (x)=K(Σ_(i)w_(i)n_(i))(x)),where K (commonly referred to as an “activation function”) is apredetermined coefficient such as a sigmoidal function, a hyperbolictangent function, and a rectified linear unit (ReLU).

In FIG. 5A (and similarly in FIG. 5B), the neurons (that is, nodes) aredepicted by circles around a threshold function. In the non-limitingexample illustrated in FIG. 5A, the inputs are depicted by circlesaround a linear function and the arrows indicate directed connectionsbetween neurons. In a specific embodiment, the ANN is a feedforwardnetwork as exemplified in FIG. 5A and FIG. 5B (for example, it can berepresented as a directed acyclic graph).

The ANN operates to achieve a specific task, such as denoising of a CTimage, by searching within the class of a function F to learn, using aset of observation results, to find an element m* (m*∈F) which solvesthe specific task in some optical criteria (for example, stoppingcriteria used at step S263 to be described below). For example, in aspecific embodiment, this can be achieved by defining a cost functionC:F→R, such as for an optical solution expressed by the followingEquation (1) (that is, no solution having a cost less than the cost ofthe optical solution).Equation (1)C(m*)≤C(m)∀m∈F  (1)

In Equation (1), m* is the optical solution. The cost function C is ameasure of how far away a particular solution is from an opticalsolution to a problem to be solved (for example, an error). Learningalgorithms iteratively search through the solution space to fine afunction with the smallest possible cost. In a specific embodiment, thecost is minimized over a sample of the data (that is, the trainingdata).

FIG. 5B illustrates a non-limiting example in which the ANN is a DCNN.The DCNN is a type of ANN having beneficial properties for imageprocessing, and, therefore, has a particular relevance for applicationsof image denoising. The DCNN uses a feedforward ANN in which aconnectivity pattern between neurons can represent convolutions in imageprocessing. For example, the DCNN can be used for image processingoptimization by using multiple layers of small neuron collections thatprocess portions of an input image, called receptive fields. The outputsof these collections can then be tiled so that they overlap, to achievea better representation of the original image. This processing patterncan be repeated over multiple layers having alternating convolution andpooling layers. Note that FIG. 2B illustrates an example of a fullyconnected (full connect) network that defines a node of a succeedinglayer by using all the nodes of a preceding layer. This example onlyillustrates an example of a deep neural network (DNN). It is common forthe DCNN to form a loosely connected (partial connect) network thatdefines a node of a succeeding layer by using some of the nodes of apreceding layer.

FIG. 5C illustrates an example of a 5×5 kernel being applied to mapvalues from an input layer representing a two-dimensional image to afirst hidden layer which is a convolution layer. The kernel mapsrespective 5×5 pixel regions to corresponding neurons of the firsthidden layer.

Following after the convolution layer, the DCNN can include local and/orglobal pooling layers that combine the outputs of neuron clusters in theconvolution layers. Moreover, in a specific embodiment, the DCNN canalso include various combinations of convolutional and fully connectedlayers, with pointwise nonlinearity applied at the end of or after eachlayer.

The DCNN has several advantages for image processing. To reduce thenumber of free parameters and improve generation, a convolutionoperation on small regions of input is introduced. One significantadvantage of the specific embodiment of the DCNN is the use of sharedweights in the convolution layer, that is, filters (weight banks) usedas coefficients for each pixel in the layer are the same. Suchsignificant advantages reduce a memory footprint and improveperformance. Compared to other image processing methods, the DCNNadvantageously uses relatively little pre-processing. This means thatthe DCNN is responsible for learning manually designed filters intraditional algorithms. The lack of dependence on prior knowledge andhuman effort in designing features is a major advantage for the DCNN.

In the DCNN, it is possible to utilize similarities between adjacentlayers in reconstructed images. The signal in the adjacent layers isordinarily highly correlated, whereas the noise is not. In general, athree-dimensional volumetric image in CT can provide more diagnosticinformation than a single slice that transverses a two-dimensional imagebecause more volumetric features can be captured. FIG. 4C illustrates anexemplary training for denoising of a two-dimensional reconstructedimage, but denoising that further uses volumetric characteristics may betrained.

FIG. 5D illustrates an exemplary embodiment of supervised learning usedto train the DCNN. In the supervised learning, a set of training data isacquired, and the network is iteratively updated to reduce errors, suchthat the synthesized subject data processed by the DCNN closely matchesthe third subject data. In other words, the DCNN infers mapping impliedby the training data, and the cost function produces an error valuerelated to mismatch between the third subject data and denoised dataproduced by applying a current incarnation of the DCNN to thesynthesized subject data. For example, in a specific embodiment, thecost function can use a mean-squared error to optimize an averagesquared error. In the case of multilayer perceptrons (MLP) neuralnetwork, a backpropagation algorithm can be used for training thenetwork by minimizing the mean-squared-error-based cost function using agradient descent method.

Training a neural network model essentially means selecting one modelfrom the set of allowed models (or determining a distribution over theset of allowed models in a Bayesian framework) that minimize the costcriterion (that is, an error value calculated using the cost function).In general, DL networks can be trained using any of numerous algorithmsfor training neural network models (for example, applying optimizationtheory or statistical estimation).

For example, the optimization method used in training artificial neuralnetworks can use some form of gradient descent, using backpropagation tocompute actual gradients. This is done by taking the derivative of thecost function with respect to network parameters and then changing thoseparameters in a gradient-related direction. The backpropagationalgorithm may be a steepest descent method (for example, with variablelearning rate, with variable learning rate and momentum, and resilientbackpropagation), a quasi-Newton method (for example,Broyden-Fletcher-Goldfarb-Shanno, one step secant, andLevenberg-Marquardt), or a conjugate gradient method (for example,Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart, andscaled conjugate gradient). Moreover, evolutionary methods, such as geneexpression programming, simulated annealing, expectation-maximization,non-parametric methods, and particle swarm optimization, can also beused for training the DCNN.

At step S210 of FIG. 5D, an initial guess is generated for thecoefficients of the DCNN. For example, the initial guess may be based ona prior knowledge of a region being imaged or one or more denoisingmethods, edge detection methods, and/or blob detection methods.Moreover, the initial guess may be based on a DCNN trained on trainingdata related to a different noise level or using a different CT scanmethod.

Exemplary denoising methods include linear smoothing filters,anisotropic diffusion, non-local means, or nonlinear filters. The linearsmoothing filters remove noise by convolving the original image with amask representing a low-pass filter or smoothing operation. For example,the Gaussian mask includes elements determined by a Gaussian function.This convolution brings the values of each pixel into closer to thevalues of pixels adjacent to the pixels. The anisotropic diffusionremoves noise while preserving sharp boundaries by evolving an imageunder a smoothing partial differential equation similar to the heatconduction equation. A median filter is an example of a nonlinear filterand, when properly designed, the nonlinear filter can also preserveboundaries and avoid burring. The median filter is an example of arank-conditioned rank-selection (RCRS) filter, which can be applied toremove salt and pepper noise from an image without introducingsignificant blurring artifacts. Moreover, a filter using atotal-variation (TV) minimization regularization term can be used whenan imaged region supports an assumption of uniformity over large areasdemarked by sharp boundaries between uniform areas. The TV filter isanother example of the nonlinear filter. In addition, non-local meansfiltering is an exemplary method of determining denoised pixels by usinga weighted average over similar patches in an image.

At step S220 of FIG. 5D, an error (for example, a cost function) iscalculated between the network processed synthesized subject data andthe third subject data. The error can be calculated using any known costfunction or distance measure between image data, including those costfunctions described above.

At step S230 of FIG. 5D, a change in the error can be calculated as afunction of a change in the network (for example, an error gradient),and this change in the error can be used to select a direction and stepsize for a subsequent change to the weights/coefficients of the DCNN.Calculating the gradient of the error in this manner is consistent withspecific embodiments of a gradient descent optimization method. In otherspecific embodiments, as would be understood by a person skilled in theart, this step may be omitted and/or replaced with another step inaccordance with another optimization algorithm (for example, anon-gradient descent optimization algorithm like simulated annealing ora genetic algorithm).

At step S240 of FIG. 5D, a new set of coefficients are determined forthe DCNN. For example, the weights/coefficients can be updated using thechange calculated at step S230, as in a gradient descent optimizationmethod or an over-relaxation acceleration method.

At step S250 of FIG. 5D, a new error value is calculated using theupdated weights/coefficients of the DCNN.

At step S260 of FIG. 5D, predetermined stopping criteria are used todetermine whether the training of the network is complete. For example,the predetermined stopping criteria can determine whether the new errorand/or the total number of iterations performed exceeds a threshold. Forexample, the stopping criteria can be satisfied when the new error fallsbelow a predetermined threshold or a maximum number of iterations isreached. When the stopping criteria are not satisfied, the procedurereturns to step S230 to repeat the process, that is, the procedure willbe continued back to the start of the iterative loop by using the newweights/coefficients (the iterative loop includes steps S230, S240,S250, and S260). When the stopping criteria are satisfied, the trainingof the DCNN is completed.

In addition to the embodiment for error minimization illustrated in FIG.5D, the training of the DCNN can use one of many other knownminimization methods including, for example, local minimization methods,convex optimization methods, and global optimization methods.

When the cost function (for example, the error) has a local minimumdifferent from the global minimum, a robust stochastic optimizationprocess is beneficial to find the global minimum of the cost function.An example of an optimization method for finding a local minimum can bea Nelder-Mead simplex method, a gradient descent method, a Newton'smethod, a conjugate gradient method, a shooting method, and one of otherknown local optimization methods. There are also many known methods forfinding global minima, including generic algorithms, simulatedannealing, exhaustive searches, interval methods, and other relateddeterministic, stochastic, heuristic, and metaheuristic method. Any ofthese methods can be used to optimize the weights/coefficients of theDCNN. Moreover, neural networks can also be optimized using abackpropagation method.

For example, the model generation function 144 d performs residuallearning an input of which is the synthesized subject data and a targetof which is the third subject data. In the residual learning, adifference between input data including noises and target data islearned. In the case of a clinically obtained noise image, noisesincluded in the image have a statistical dependency on an image signal,but noises added to the synthesized subject data do not have suchdependency. However, in the residual learning, the difference betweenthe input data and the target data and characteristics of the noisesitself are more important factors than the dependency of the noises onthe image signal. Accordingly, the model generation function 144 d cantrain the DCNN with the same degree of accuracy as when the synthesizedsubject data is input and the clinically obtained noise image is input.

Here, the third subject data may be noisy data or clean data. That is,the model generation function 144 d may perform noise-to-noise trainingor noise-to-clean training for the DCNN.

For example, the projection data Yk−1, the projection data Yk, and theprojection data Yk+1 illustrated in FIG. 4B may be projection dataimaged using a low dose of X-rays. That is, the second subject data andthe third subject data may be data obtained with low-dose imaging.Furthermore, for example, the acquisition function 144 c may alsoacquire the third subject data by using a reconstruction method otherthan a high accurate reconstruction method such as the successiveapproximation reconstruction method. As an example, the acquisitionfunction 144 c performs the FBP as the reconstruction processing R2illustrated in FIG. 4B. With this, the acquisition function 144 c setsthe third subject data stored in the image pool 141 c as noisy data. Insuch a case, since noises included in the third subject data and noisesbased on the noise data added to the synthesized subject data areindependent, the model generation function 144 d can perform thenoise-to-noise training to acquire a DCNN.

Furthermore, for example, the acquisition function 144 c acquires thesecond subject data by performing the reconstruction processing based ona first reconstruction method and acquires the third subject data byperforming the reconstruction processing based on a secondreconstruction method with higher accuracy than the first reconstructionmethod. As an example, the acquisition function 144 c performs the FBPas the reconstruction processing R1 illustrated in FIG. 4B, and performsthe successive approximation reconstruction method as the reconstructionprocessing R2. With this, the acquisition function 144 c can use thethird subject data stored in the image pool 141 c as clean data, and themodel generation function 144 d can perform the noise-to-clean trainingto acquire a DCNN. Note that the DCNN in such a case performs trainingan input of which is an image based on the FBP method and a target ofwhich is an image based on the successive approximation reconstructionmethod. That is, the DCNN can learn a difference depending on thereconstruction method. Accordingly, the model generation function 144 dcan function the DCNN to reduce noises in the input data and improve theresolution.

The model generation function 144 d may generate a DCNN for each partsuch as the lung, abdomen, and pelvis. For example, the model generationfunction 144 d may perform training by using data of the lung as thesynthesized subject data or the third subject data, thereby obtaining aDCNN. The DCNN in such a case is a learned model specialized for thenoise reduction processing of an image obtained by imaging the lung.

Alternatively, the model generation function 144 d may perform trainingby using data of various parts as the synthesized subject data or thethird subject data, thereby obtaining a DCNN. The DCNN in such a case isa general-purpose learned model that receives the input of an imageobtained by imaging an arbitrary part and performs the noise reductionprocessing.

Furthermore, the model generation function 144 d may generate a DCNN foreach noise level. For example, the acquisition function 144 c acquiresnoise data based on the first subject data imaged at a predetermineddose, and generates the noise pool 141 a. Furthermore, for example, theacquisition function 144 c adjusts the value of a parameter a such thatthe noise level has a predetermined value, and generates the noise pool141 a. Furthermore, the model generation function 144 d acquires thesynthesized subject data on the basis of the noise data read from thenoise pool 141 a and the second subject data. With this, the modelgeneration function 144 d can allow the noise level of the noises addedto the synthesized subject data to be substantially constant. Then, themodel generation function 144 d performs training by using thesynthesized subject data and the third subject data, thereby acquiring aDCNN. The DCNN in such a case is a learned model specialized for thenoise reduction processing of an image obtained by imaging at apredetermined dose.

Alternatively, the model generation function 144 d may perform trainingby using synthesized subject data of various noise levels, therebyobtaining a DCNN. The DCNN in such a case is a general-purpose learnedmodel that receives the input of an image obtained by imaging at anarbitrary dose and performs the noise reduction processing.

Furthermore, the model generation function 144 d may generate a DCNN foreach image size. For example, the model generation function 144 d mayperform training by using the synthesized subject data or the thirdsubject data cut in a predetermined size, thereby obtaining a DCNN.Alternatively, the model generation function 144 d may perform trainingby using the synthesized subject data or the third subject data havingvarious image sizes, thereby obtaining a DCNN.

As described above, the model generation function 144 d acquires a DCNNby machine learning using the synthesized subject data and the thirdsubject data, and allows the learned DCNN to be stored in the memory141. Thereafter, for example, when input subject data is obtained byimaging a subject P12, the noise reduction processing function 144 e canperform the noise reduction process of the input subject data by usingthe DCNN read from the memory 141. Note that the subject P12 may be asubject different from the projection data Yk−1, the projection data Yk,and the projection data Yk+1 illustrated in FIG. 4A and the projectiondata Yl−1, the projection data Yl, and the projection data Yl+1illustrated in FIG. 4B, or may be the same subject. The subject P12 isan example of the subject P1.

Specifically, the imaging function 144 b images the subject P12 andacquires projection data. Furthermore, the noise reduction processingfunction 144 e performs the reconstruction processing based on the FBPmethod and generates a reconstructed image. The reconstructed image isan example of the input subject data. Next, the noise reductionprocessing function 144 e reduces noises in the reconstructed image bythe DCNN read from the memory 141, thereby obtaining denoised data.

Hereinafter, a noise reduction process using the DCNN will be describedin detail. FIG. 6A and FIG. 6B illustrate the noise reduction processaccording to a first embodiment.

FIG. 6A is general for all ANNs and FIG. 6B is particular to CNNs. Aseries of processes in FIG. 6A corresponds to applying the DCNN to theinput subject data. Following after a convolution layer, the DCNN caninclude local and/or global pooling layers, which combine the outputs ofneuron clusters in the convolution layers.

At step S410, the weights/coefficients corresponding to the connectionsbetween neurons (that is, nodes) are applied to the respective inputscorresponding to the pixels of the reconstructed image.

At step S420, the weighted inputs are summed. When only non-zeroweights/coefficients connecting to a predetermined neuron on the nextlayer are regionally localized in an image represented in the previouslayer, the combination of steps S410 and S420 is essentially identicalto performing a convolution operation.

At step S430, respective thresholds are applied to the weighted sums ofthe respective neurons.

At step S440, the steps of weighting, summing, and activating arerepeated for each of the subsequent layers.

FIG. 6B illustrates a flow schematic diagram of another embodiment ofthe noise reduction process using the DCNN. The embodiment of step S170illustrated in FIG. 6B corresponds to an operation on the reconstructedimage using a non-limiting embodiment of a CNN for the DCNN.

At step S450, calculations for a convolution layer are performed asdescribed above according to the understanding of a person skilled inthe art in convolution layers.

At step S460, the outputs from the convolution layer are the inputs intoa pooling layer. The pooling layer is performed according to theaforementioned description of pooling layers and is performed accordingto the understanding of a person skilled in the art in pooling layers.

At step S470, the steps of a convolution layer followed by a polinglayer can be repeated a predetermined number of layers. Following (orintermixed with) the mixed convolution and poling layers, the outputfrom a poling layer can be fed to a predetermined number of ANN layersperformed according to the description provided for the ANN layers inFIG. 6A. The final output will be a desired reconstructed image(denoised data) characterized by no noise/artifact.

Then, the output function 144 f outputs an image of the subject P12based on the denoised data. For example, the output function 144 fgenerates a display image on the basis of the denoised data and allowsthe display 142 to display the display image. Alternatively, the outputfunction 144 f may transmit the image of the subject P12 based on thedenoised data to an external device such as a workstation.

Next, an example of the processing procedure by the X-ray CT apparatus10 will be described with reference to FIG. 7 . FIG. 7 is a flowchartfor explaining a series of flows of the process of the X-ray CTapparatus 10 according to the first embodiment. Step S101, step S102,and step S107 correspond to the acquisition function 144 c. Step S103corresponds to the model generation function 144 d. Step S104 and stepS105 correspond to the noise reduction processing function 144 e. StepS106 corresponds to the output function 144 f.

First, the processing circuitry 144 acquires the noise data on the basisof the first subject data (step S101), and acquires the synthesizedsubject data on the basis of the second subject data and the noise data(step S102). Next, the processing circuitry 144 acquires the noisereduction processing model such as the DCNN by the machine learningusing the synthesized subject data and the third subject data (stepS103).

Next, the processing circuitry 144 determines whether the input subjectdata obtained by imaging the subject P12 has been acquired (step S104).When the input subject data has been acquired (Yes at step S104), theprocessing circuitry 144 reduces noises in the input subject data by thenoise reduction processing model to acquire denoised data (step S105).Furthermore, the processing circuitry 144 outputs the image of thesubject P12 based on the denoised data (step S106).

Here, the processing circuitry 144 determines whether to update trainingdata (step S107). When updating the training data (Yes at step S107),the processing circuitry 144 proceeds to step S101 again. That is, whenupdating the training data, the processing circuitry 144 sets dataobtained by imaging the subject P12 as the first subject data, acquiresthe noise data in the first subject data, and adds the noise data to thenoise pool 141 a. Alternatively, the processing circuitry 144 may setthe data obtained by imaging the subject P12 as the second subject dataor the third subject data, and add the second subject data or the thirdsubject data to the image pool 141 b or the image pool 141 c. On theother hand, when not updating the training data (No at step S107), theprocessing circuitry 144 proceeds to step S104 again. Furthermore, whenthe input subject data is not acquired at step S104 (No at step S104),the processing circuitry 144 ends the process.

As described above, according to the first embodiment, on the basis ofthe first subject data obtained by the imaging performed by the X-ray CTapparatus 10, the acquisition function 144 c acquires the noise data inthe first subject data. Furthermore, on the basis of the second subjectdata and the noise data acquired by the imaging performed by a same kindof medical image diagnostic modality (X-ray CT) as the X-ray CTapparatus 10, the acquisition function 144 c acquires the syntheticsubject data in which noises based on the noise data are added to thesecond subject data. Furthermore, the model generation function 144 dacquires the noise reduction processing model by machine learning usingthe synthetic subject data and the third subject data acquired by theimaging performed by the X-ray CT. With this, the X-ray CT apparatus 10according to the first embodiment can easily acquire a high-qualitynoise reduction processing model.

For example, the first subject data, the second subject data, and thethird subject data described above do not need to be clean data acquiredusing a high dose of X-rays, and can be acquired relatively easily.Furthermore, since the synthesized subject data is acquired by combiningthe noise data and the second subject data, it is easy to prepare arequired number of data for training. Accordingly, the X-ray CTapparatus 10 can easily prepare training data and improve the quality ofthe noise reduction processing model with sufficient training data.

Furthermore, when the noise reduction processing model is generatedusing noises generated by the simulation as the training data, thequality of the noise reduction processing model also changes accordingto the accuracy of the noise simulation. On the other hand, the noisesin the aforementioned noise data are not simulated, but are extractedfrom the clinically obtained first subject data. That is, the X-ray CTapparatus 10 can generate the noise reduction processing model by usingmore reliable training data and improve the performance of the noisereduction processing.

So far, although the first embodiment has been described, it may beimplemented in various different forms other than the aforementionedembodiment.

For example, in FIG. 3 , it has been described that the projection dataY1 is sampled to acquire two pieces of projection data (the projectiondata Y11 and the projection data Y12). However, the embodiment is notlimited thereto. For example, the acquisition function 144 c may acquirethree or more pieces of projection data by sampling the projection dataY1.

As an example, by sampling the projection data Y1, the acquisitionfunction 144 c acquires “3n (n is a natural number)” views in theprojection data Y1 as the projection data Y11, acquires “3(n+1)” viewsin the projection data Y1 as the projection data Y12, and acquires“3(n+2)” views in the projection data Y1 as projection data Y13.Furthermore, the acquisition function 144 c reconstructs thereconstructed image X11 from the projection data Y11, reconstructs thereconstructed image X12 from the projection data Y12, and reconstructs areconstructed image X13 from the projection data Y13.

Then, the acquisition function 144 c performs noise extractionprocessing on the basis of the reconstructed image X11, thereconstructed image X12, and the reconstructed image X13. For example,the acquisition function 144 c acquires noise data by performingdifference processing between the reconstructed image X11 and thereconstructed image X12. Furthermore, the acquisition function 144 cacquires noise data by performing difference processing between thereconstructed image X12 and the reconstructed image X13. Furthermore,the acquisition function 144 c acquires noise data by performingdifference processing between the reconstructed image X13 and thereconstructed image X11.

Furthermore, in FIG. 3 , the case has been described in which aplurality of reconstructed images are generated and noise data isextracted by performing difference processing between images. However,the extraction method of the noise data is not limited thereto. Forexample, the acquisition function 24 b may omit the sampling, generatethe reconstructed images based on the projection data Y1, and extractthe noise data by performing image processing on the reconstructedimages.

Furthermore, in FIG. 4B, it has been described that the second subjectdata to be stored in the image pool 141 b and the third subject data tobe stored in the image pool 141 c are respectively generated byperforming the reconstruction processing R1 and the reconstructionprocessing R2. However, the embodiment is not limited thereto. Forexample, the acquisition function 144 c may allow data based on a firstsubset of the projection data such as the projection data Yl−1, theprojection data Y1, and the projection data Yl+1 to be stored in theimage pool 141 b as the second subject data and allow data based on asecond subset different from the first subset to be stored in the imagepool 141 c as the third subject data. In other words, on the basis ofsubject data of a certain subject, the acquisition function 144 c maygenerate second subject data corresponding to a first subset of thesubject data and third subject data corresponding to a second subsetdifferent from the first subset.

As an example, the acquisition function 144 c acquires the first subsetby sampling odd view data in the projection data Yl−1, and allows areconstructed image based on the first subset to be stored in the imagepool 141 b as the second subject data. Furthermore, the acquisitionfunction 144 c acquires the second subset by sampling even view data inthe projection data Yl−1, and allows a reconstructed image based on thesecond subset to be stored in the image pool 141 c as the third subjectdata. Although the case where the sampling is performed separately forthe odd view data and the even view data, the sampling method can bearbitrarily changed.

Furthermore, in FIG. 4B, it has been described that the second subjectdata and the third subject data are generated from the same projectiondata. However, the embodiment is not limited thereto. For example, theacquisition function 144 c may generate only the second subject data onthe basis of the projection data Yl−1, and may generate only the thirdsubject data on the basis of the projection data Yl. That is, the imagepool 141 b and the image pool 141 c may be generated from differentpieces of projection data.

Furthermore, in the aforementioned embodiment, although the secondsubject data and the third subject data have been described as differentpieces of data, the second subject data and the third subject data maybe the same data. For example, the acquisition function 144 c acquiresthe synthesized subject data on the basis of the noise data stored inthe noise pool 141 a and the data stored in the image pool 141 b. Then,the model generation function 144 d can obtain a DCNN by performingtraining using the synthesized subject data and the data stored in theimage pool 141 b.

Furthermore, in the aforementioned embodiment, the DCNN, which receivesthe input of the reconstructed image and performs the noise reductionprocessing, has been described as an example of the noise reductionprocessing model. However, the embodiment is not limited thereto. Forexample, the model generation function 144 d may generate, as the noisereduction processing model, a DCNN that receives the input of projectiondata such as a sinogram and performs the noise reduction processing.

For example, similarly to the case illustrated in FIG. 4A, theacquisition function 144 c first performs noise extraction processing oneach of the projection data such as the projection data Yk−1, theprojection data Yk, and the projection data Yk+1, thereby generatingvolume data indicating a noise distribution. Next, the acquisitionfunction 144 c generates forward projection data in which the volumedata indicating the noise distribution has been forward projected foreach of a plurality of views. Such forward projection data is, forexample, a sinogram indicating the noise distribution. Furthermore, suchforward projection data is an example of the noise data in the firstsubject data. In other words, the noise data may be data indicatingnoise intensity at each position in the projection data space. Theacquisition function 144 c generates a plurality of forward projectiondata, which are the noise data, and allows the forward projection datato be stored in the noise pool 141 a.

Furthermore, similarly to the case illustrated in FIG. 4B, theacquisition function 144 c generates a reconstructed image by performingthe reconstruction processing R1 on each of the projection data such asthe projection data Yl−1, the projection data Yl, and the projectiondata Yl+1. Next, the acquisition function 144 c generates forwardprojection data in which the generated reconstructed image has beenforward projected for each of a plurality of views. Such forwardprojection data is, for example, a sinogram having a quality accordingto the reconstruction processing R1. Furthermore, such forwardprojection data is an example of the second subject data. Theacquisition function 144 c generates a plurality of forward projectiondata, which are the second subject data, and allows the forwardprojection data to be stored in the image pool 141 b.

Furthermore, similarly to the case illustrated in FIG. 4B, theacquisition function 144 c generates a reconstructed image by performingthe reconstruction processing R2 on each of the projection data such asthe projection data Yl−1, the projection data Yl, and the projectiondata Yl+1. Next, the acquisition function 144 c generates forwardprojection data in which the generated reconstructed image has beenforward projected for each of a plurality of views. Such forwardprojection data is, for example, a sinogram having a quality accordingto the reconstruction processing R2. Furthermore, such forwardprojection data is an example of the third subject data. The acquisitionfunction 144 c generates a plurality of forward projection data, whichare the third subject data, and allows the forward projection data to bestored in the image pool 141 c.

Next, the acquisition function 144 c acquires the synthesized subjectdata on the basis of the noise data read from the noise pool 141 a andthe second subject data read from the image pool 141 b. Such synthesizedsubject data is, for example, a sinogram to which noises based on thenoise data have been added. Then, the model generation function 144 dobtains a DCNN by training a model by deep learning an input of which isthe synthesized subject data and a target of which is the third subjectdata. The DCNN in such a case is functionalized to receive the input ofprojection data obtained by imaging the subject P12, for example, and toreduce noises in the projection data. Note that the projection dataobtained by imaging the subject P12 is an example of input subject data.

For example, the imaging function 144 b acquires the projection data byimaging the subject P12. Furthermore, the noise reduction processingfunction 144 e reduces noises in the projection data by the DCNN andobtain denoised data. Then, the output function 144 f outputs the imageof the subject P12 based on the denoised data. For example, the outputfunction 144 f performs reconstruction processing on the denoised dataand generates a reconstructed image. Moreover, the output function 144 fgenerates a display image on the basis of the reconstructed image andallows the display 142 to display the display image. Alternatively, theoutput function 144 f may transmit the reconstructed image and thedisplay image to an external device such as a workstation.

Furthermore, in the aforementioned embodiment, the noise reductionprocessing model has been described as being configured by the DCNN.However, the embodiment is not limited thereto. For example, the noisereduction processing function 144 e may configure the noise reductionprocessing model by another type of neural network such as a fullyconnected neural network and a recurrent neural network (RNN).Furthermore, the noise reduction processing function 144 e may generatethe noise reduction processing model by a machine learning method otherthan the neural network. For example, the noise reduction processingfunction 144 e may generate the noise reduction processing model byperforming machine learning using an algorithm such as logisticregression analysis, nonlinear discriminant analysis, support vectormachine (SVM), random forest, and naive Bayes.

Furthermore, in the aforementioned embodiment, the X-ray CT has beendescribed as an example of the medical image diagnostic modality.However, the embodiment is not limited thereto, and similar processingcan also be performed on information acquired by imaging performed byanother medical image diagnostic modality. For example, theaforementioned embodiment can also be similarly applied to informationacquired by imaging performed by an X-ray diagnostic apparatus, magneticresonance imaging (MRI), ultrasonic imaging, and imaging performed by asingle photon emission computed tomography (SPECT), a positron emissioncomputed tomography (PET), and the like.

Furthermore, in the aforementioned embodiment, the case has beendescribed in which the processing circuitry 144 in the X-ray CTapparatus 10 performs various functions such as the acquisition function144 c, the model generation function 144 d, the noise reductionprocessing function 144 e, and the output function 144 f. However, theembodiment is not limited thereto. For example, processing circuitryincluded in an apparatus different from the X-ray CT apparatus 10 mayperform functions corresponding to the respective functions of theprocessing circuitry 144.

Hereinafter, this point will be described with reference to FIG. 8 .FIG. 8 is a block diagram illustrating an example of a configuration ofan information processing system 1 according to a second embodiment. Forexample, the information processing system 1 includes an X-ray CTapparatus 10 and an information processing apparatus 20 as illustratedin FIG. 8 . The X-ray CT apparatus 10 and the information processingapparatus 20 are connected to each other via a network NW.

Note that the location where the X-ray CT apparatus 10 and theinformation processing apparatus 20 are installed is arbitrary as longas they can be connected via the network NW. For example, the X-ray CTapparatus 10 and the information processing apparatus 20 may beinstalled within facilities different from each other. That is, thenetwork NW may be a local network closed within the facility or anetwork via the Internet. Furthermore, communication between the X-rayCT apparatus 10 and the information processing apparatus 20 may beperformed via another apparatus such as an image storage apparatus, ormay be directly performed without using another apparatus. An example ofsuch an image storage apparatus includes a picture archiving andcommunication system (PACS) server, for example.

The X-ray CT apparatus 10 illustrated in FIG. 8 has the sameconfiguration as that of the X-ray CT apparatus 10 illustrated in FIG. 1. However, the processing circuitry 144 of the X-ray CT apparatus 10illustrated in FIG. 8 may or may not have such functions as theacquisition function 144 c, the model generation function 144 d, thenoise reduction processing function 144 e, and the output function 144f. Furthermore, although FIG. 8 illustrates the X-ray CT apparatus 10 asan example of a medical image diagnostic apparatus, the informationprocessing system 1 may include a medical image diagnostic apparatusdifferent from the X-ray CT apparatus 10. Furthermore, the informationprocessing system 1 may include a plurality of medical image diagnosticapparatuses.

The information processing apparatus 20 performs various processes onthe basis of data acquired by the X-ray CT apparatus 10. For example, asillustrated in FIG. 8 , the information processing apparatus 20 includesa memory 21, a display 22, an input interface 23, and processingcircuitry 24.

The memory 21 can be configured similarly to the aforementioned memory141. For example, the memory 21 stores a computer program required whencircuitry included in the information processing apparatus 20 performsits functions. Furthermore, the memory 21 stores the noise datasimilarly to the noise pool 141 a. Furthermore, the memory 21 stores thesecond subject data similarly to the image pool 141 b. Furthermore, thememory 21 stores the third subject data similarly to the image pool 141c.

The display 22 can be configured similarly to the aforementioned display142. For example, the display 22 displays a GUI for receiving variousinstructions, settings, and the like from a user. Furthermore, forexample, the display 22 displays an image based on denoised data inwhich noises have been reduced by the noise reduction processing model.The information processing apparatus 20 may include a projector insteadof or in addition to the display 22.

The input interface 23 can be configured similarly to the aforementionedinput interface 143. For example, the input interface 23 receivesvarious input operations from a user, converts the received inputoperations into electrical signals, and outputs the electrical signalsto the processing circuitry 24.

The processing circuitry 24 controls the overall operation of theinformation processing apparatus 20 by performing a control function 24a, an acquisition function 24 b, a model generation function 24 c, anoise reduction processing function 24 d, and an output function 24 e.For example, the control function 24 a controls various functions suchas the acquisition function 24 b, the model generation function 24 c,the noise reduction processing function 24 d, and the output function 24e on the basis of the various input operations received from the uservia the input interface 23. The acquisition function 24 b is a functioncorresponding to the acquisition function 144 c. The model generationfunction 24 c is a function corresponding to the model generationfunction 144 d. The noise reduction processing function 24 d is afunction corresponding to the noise reduction processing function 144 e.The output function 24 e is a function corresponding to the outputfunction 144 f.

In the information processing apparatus 20 illustrated in FIG. B,respective processing functions are stored in the memory 21 in the formof computer programs that can be executed by a computer. The processingcircuitry 24 is a processor that reads and executes the computerprograms from the memory 21, thereby performing functions correspondingto the computer programs. In other words, the processing circuitry 24having read the computer programs has the functions corresponding to theread computer programs.

Note that, in FIG. 8 , it has been described that the control function24 a, the acquisition function 24 b, the model generation function 24 c,the noise reduction processing function 24 d, and the output function 24e are performed by the single processing circuitry 24, but theprocessing circuitry 24 may be configured by combining a plurality ofindependent processors, and each processor may be configured to performeach function by executing each computer program. Furthermore, eachprocessing function of the processing circuitry 24 may be performed bybeing appropriately distributed or integrated into a single processingcircuit or a plurality of processing circuits.

Furthermore, the processing circuitry 24 may also perform the functionsby using a processor of an external device connected via the network NW.For example, the processing circuitry 24 reads and executes the computerprograms corresponding to the functions from the memory 21 and uses, ascomputation resources, a server group (cloud) connected to theinformation processing apparatus 20 via the network NW, therebyperforming the functions illustrated in FIG. 8 .

For example, on the basis of first subject data obtained by imagingperformed by a medical image diagnostic apparatus such as the X-ray CTapparatus 10, the acquisition function 24 b acquires noise data in thefirst subject data. Furthermore, on the basis of second subject dataobtained by the imaging performed by the medical image diagnosticapparatus and the noise data in the first subject data, the acquisitionfunction 24 b acquires synthesized subject data in which noises based onthe noise data are added to the second subject data. Furthermore, themodel generation function 24 c obtains a noise reduction processingmodel by machine learning using the synthesized subject data and thirdsubject data obtained by the imaging performed by the medical imagediagnostic apparatus. Furthermore, the noise reduction processingfunction 24 d reduces noises on input subject data obtained by theimaging performed by the medical image diagnostic apparatus such as theX-ray CT apparatus 10, by the noise reduction processing model, therebyobtaining denoised data. Furthermore, the output function 24 e outputsan image based on the denoised data.

In the method according to the aforementioned embodiment, as trainingdata to be used when training one DCNN, only an image acquired byimaging a specific site (chest, abdomen, head, and the like) may betargeted, instead of targeting all images. In such a case, the DCNN isprovided for each site. Alternatively, only an image acquired usingimaging parameters/reconstructed parameters (scan protocols) for aspecific diagnostic purpose may be targeted. In such a case, the DCNN isprepared for each site or for each diagnostic purpose, for example, foreach scan protocol and stored in the memory, and the medical imagediagnostic apparatus selects a trained DCNN according to the siteselected at the time of imaging and the diagnostic purpose (scanprotocol), and performs the noise reduction process on an image, whichis acquired by the scan protocol, with the selected DCNN. By so doing,it is possible to achieve effective noise reduction with a DCNNspecialized for noise more specific to a specific site or diagnosticpurpose (scan protocol).

The term “processor” used in the above description, for example, means acircuit such as a CPU, a graphics processing unit (GPU), an applicationspecific integrated circuit (ASIC), and a programmable logic device (forexample, a simple programmable logic device (SPLD), a complexprogrammable logic device (CPLD), and a field programmable gate array(FPGA)). When the processor is, for example, the CPU, the processorperforms functions by reading and executing computer programs stored ina storage circuit. On the other hand, when the processor is, forexample, the ASIC, the functions are directly incorporated in thecircuit of the processor as a logic circuit instead of storing thecomputer programs in the storage circuit. Note that each processor ofthe embodiment is not limited to a case where each processor isconfigured as a single circuit, and one processor may be configured bycombining a plurality of independent circuits to perform functionsthereof. Moreover, a plurality of components in each drawing may beintegrated into one processor to perform functions thereof.

Furthermore, in FIG. 1 , it has been described that the single memory141 stores the computer programs corresponding to the respectiveprocessing functions of the processing circuitry 144. Furthermore, inFIG. 8 , it has been described that the single memory 21 stores thecomputer programs corresponding to the respective processing functionsof the processing circuitry 24. However, the embodiment is not limitedthereto. For example, a plurality of memories 141 may be arranged in adistributed manner, and the processing circuitry 144 may be configuredto read corresponding computer programs from the individual memories141. Similarly, a plurality of memories 21 may be arranged in adistributed manner, and the processing circuitry 24 may be configured toread corresponding computer programs from the individual memories 21.Furthermore, instead of storing the computer programs in the memory 141or the memory 21, the computer programs may be directly incorporated inthe circuit of the processor. In such a case, the processor reads andexecutes the computer programs incorporated in the circuit to performfunctions thereof.

Each component of each apparatus according to the aforementionedembodiment is functionally conceptual, and does not necessarily need tobe physically configured as illustrated in the drawings. That is, thespecific form of distribution and integration of each apparatus is notlimited to that illustrated in the drawing and all or some thereof canbe functionally or physically distributed and integrated in arbitraryunits according to various loads, usage conditions, and the like.Moreover, all or some of the processing functions performed by eachapparatus may be performed by the CPU and the computer programs that areanalyzed and executed by the CPU, or may be performed as a wiredlogic-based hardware.

Furthermore, the information processing method described in theaforementioned embodiment can be implemented by executing an informationprocessing program prepared in advance on a computer such as a personalcomputer and a workstation. The information processing program can bedistributed via a network such as the Internet. Furthermore, theinformation processing program can be executed by being recorded on anon-transitory computer readable recording medium such as a hard disk, aflexible disk (FD), a CD-ROM, an MO, and a DVD, and being read from therecording medium by the computer.

According to at least one embodiment described above, it is possible toeasily acquire a high-quality noise reduction processing model.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An information processing method of informationacquired by imaging performed by a medical image diagnostic apparatus,the information processing method comprising: based on first subjectdata acquired by the imaging performed by the medical image diagnosticapparatus, acquiring noise data in the first subject data; based onsecond subject data acquired by the imaging performed by a medical imagediagnostic modality of a same kind as the medical image diagnosticapparatus and based on the acquired noise data, acquiring synthesizedsubject data in which noise based on the acquired noise data is added tothe second subject data; and acquiring a noise reduction processingmodel by machine learning using the synthesized subject data and thirdsubject data acquired by the imaging performed by the medical imagediagnostic modality.
 2. The information processing method according toclaim 1, wherein the second subject data is acquired by the imagingperformed by the medical image diagnostic apparatus.
 3. The informationprocessing method according to claim 1, wherein the second subject datais acquired by imaging performed by another medical image diagnosticapparatus, which is a same kind as the medical image diagnosticapparatus and is not the medical image diagnostic apparatus.
 4. Theinformation processing method according to claim 1, wherein the secondsubject data is acquired by imaging performed by another medical imagediagnostic apparatus, which is a same imaging system as the medicalimage diagnostic apparatus and is not the medical image diagnosticapparatus.
 5. The information processing method according to claim 1,wherein the third subject data is data acquired by imaging a subjectthat is a same subject used to obtain the second subject data.
 6. Theinformation processing method according to claim 1, wherein the noisedata is data indicating a noise intensity of the first subject data inimage space or projection data space.
 7. The information processingmethod according to claim 1, wherein the noise reduction processingmodel is acquired by training the model by deep learning, wherein aninput of the model is the synthesized subject data and a target of themodel is the third subject data acquired by the imaging performed by themedical image diagnostic apparatus.
 8. The information processing methodaccording to claim 1, wherein the step of acquiring the noise datafurther comprises extracting the noise data based on a firstreconstructed image and a second reconstructed image acquired byreconstructing a first subset of the first subject data and a secondsubset of the first subject data, respectively.
 9. The informationprocessing method according to claim 8, wherein the extracting stepfurther comprises extracting the noise data by performing differenceprocessing between the first reconstructed image and the secondreconstructed image.
 10. The information processing method according toclaim 1, further comprising acquiring the second subject data from asubject different from the first subject data, or at a date and timedifferent from the first subject data.
 11. The information processingmethod according to claim 1, further comprising: acquiring the secondsubject data based on a first subset of fourth subject data acquired bythe imaging performed by the medical image diagnostic apparatus, andacquiring the third subject data based on a second subset of the fourthsubject data, which is different from the first subset.
 12. Theinformation processing method according to claim 1, further comprising:acquiring the second subject data as a reconstructed image acquired byreconstructing the fourth subject data acquired by the imaging performedby the medical image diagnostic apparatus, by a first reconstructionmethod, and acquiring the third subject data as a reconstructed imageacquired by reconstructing the fourth subject data by a secondreconstruction method with higher accuracy than the first reconstructionmethod.
 13. The information processing method according to claim 1,further comprising acquiring the third subject data as a reconstructedimage acquired by reconstructing the fourth subject data acquired by theimaging performed by the medical image diagnostic apparatus, by an FBPmethod.
 14. The information processing method according to claim 1,further comprising acquiring the second subject data and the thirdsubject data by low-dose imaging.
 15. A medical image diagnosticapparatus, comprising: processing circuitry configured to acquire inputsubject data by imaging a subject, acquire denoised data by reducingnoise in the input subject data by a noise reduction processing modelacquired by training a model by machine learning using synthesizedsubject data and third subject data, the synthesized subject data beingacquired based on second subject data and noise data in first subjectdata acquired by imaging performed by the medical image diagnosisapparatus, and output an image of the subject based on the denoiseddata.
 16. The medical image diagnostic apparatus according to claim 15,wherein the processing circuitry is further configured to: acquire thenoise data in the first subject data based on the first subject dataacquired by the imaging performed by the medical image diagnosticapparatus, acquire synthesized subject data, in which noise based on thenoise data is added to the second subject data, based on the secondsubject data and the noise data, the second subject data being acquiredby the imaging performed by the medical image diagnostic apparatus, andacquire the noise reduction processing model by machine learning usingthe synthesized subject data and the third subject data, which isacquired by the imaging performed by the medical image diagnosticapparatus.
 17. An information processing system, comprising: processingcircuitry configured to acquire input subject data by imaging a subject,acquire denoised data by reducing noise in the input subject data by anoise reduction processing model acquired by training a model by machinelearning using synthesized subject data and third subject data, thesynthesized subject data being acquired based on second subject data andnoise data in first subject data acquired by imaging performed by themedical image diagnosis apparatus, and output an image of the subjectbased on the denoised data.
 18. The information processing systemaccording to claim 17, wherein the processing circuitry is furtherconfigured to: acquire the noise data in the first subject data based onthe first subject data acquired by the imaging performed by a medicalimage diagnostic apparatus, acquire synthesized subject data, in whichnoise based on the noise data is added to the second subject data, basedon the second subject data and the noise data, the second subject databeing acquired by the imaging performed by the medical image diagnosticapparatus, and acquire the noise reduction processing model by machinelearning using the synthesized subject data and the third subject dataacquired by the imaging performed by the medical image diagnosticapparatus.
 19. An information processing method of processinginformation acquired by imaging performed by a medical image diagnosticmodality, the information processing method comprising: based on firstsubject data acquired by the imaging performed by a medical imagediagnostic apparatus, acquiring noise data in the first subject data;based on subject data of a subject acquired by the imaging performed bya medical image diagnostic modality of a same kind as the medical imagediagnostic apparatus, generating second subject data corresponding to afirst subset of the subject data, and third subject data correspondingto a second subset of the subject data, which is different from thefirst subset; generating synthesized subject data by synthesizing thesecond subject data and the acquired noise data; and acquiring a noisereduction processing model by machine learning, wherein an input of themodel is the synthesized subject data and a target of the model is thethird subject data.
 20. An information processing method of informationacquired by imaging performed by a medical image diagnostic apparatus,the information processing method comprising: based on first subjectdata acquired by the imaging performed by the medical image diagnosticapparatus, acquiring noise data in the first subject data; based onsecond subject data acquired by the imaging performed by a medical imagediagnostic modality of a same kind as the medical image diagnosticapparatus and based on the acquired noise data, acquiring synthesizedsubject data in which noise based on the acquired noise data is added tothe second subject data; and acquiring a noise reduction processingmodel by machine learning using the synthesized subject data and thirdsubject data acquired by the imaging performed by the medical imagediagnostic modality, wherein the step of acquiring the noise datafurther comprises extracting the noise data based on a firstreconstructed image and a second reconstructed image acquired byreconstructing a first subset of the first subject data and a secondsubset of the first subject data different from the first subset,respectively.
 21. The information processing method according to claim1, wherein the first subject data, the second subject data, and thethird subject data are acquired by imaging a human body.